{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bg5IMR11G3EE"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 761,
     "status": "ok",
     "timestamp": 1593524849710,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "jLiGhwcR82po"
   },
   "outputs": [],
   "source": [
    "text_file = open(\"./data.txt\", \"r\")\n",
    "lines = text_file.read()\n",
    "counter = 1\n",
    "data = []\n",
    "while True:\n",
    "    temp = lines.split('\\n'+str(counter)+'. ')\n",
    "    if len(temp)==2:\n",
    "        data.append(temp[0])\n",
    "        lines = temp[1]\n",
    "    else:\n",
    "        break\n",
    "    counter += 1\n",
    "data.append(lines)\n",
    "del data[0]\n",
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 990,
     "status": "ok",
     "timestamp": 1593524852899,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "BRb_hCU4Itm1"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train_id, y_test_id = train_test_split(data, range(len(data)), test_size=0.15, random_state=42) # split data to test and training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1204,
     "status": "ok",
     "timestamp": 1593524859682,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "4MIJG4WQVDWL"
   },
   "outputs": [],
   "source": [
    "xl_file = pd.ExcelFile('./label.xlsx')\n",
    "label = xl_file.parse('Sheet1')\n",
    "y_train = label.iloc[y_train_id]\n",
    "y_test = label.iloc[y_test_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 26, 44, 66, 108, 24, 42, 118, 94, 36, 90, 12, 15, 73, 22, 117, 120, 84, 30, 78, 65, 9, 33, 25, 70, 28, 121, 101, 5, 64, 69, 39, 49, 35, 16, 67, 34, 80, 7, 43, 110, 68, 81, 27, 19, 98, 107, 8, 13, 112, 3, 17, 38, 72, 95, 6, 85, 105, 97, 54, 50, 113, 46, 83, 61, 119, 79, 96, 41, 58, 48, 88, 104, 57, 75, 32, 115, 59, 63, 114, 37, 29, 109, 1, 52, 21, 2, 23, 103, 99, 87, 111, 74, 86, 82, 122, 20, 60, 71, 106, 14, 92, 51, 102]\n"
     ]
    }
   ],
   "source": [
    "print(y_train_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1214,
     "status": "ok",
     "timestamp": 1593524862123,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "6hS_ua5nMjpH",
    "outputId": "e2cab3b4-dea8-42e0-cf66-ccba60a36f4a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(104, 50) (19, 50)\n"
     ]
    }
   ],
   "source": [
    "tfidf_vect = TfidfVectorizer(ngram_range=(1,4), max_features=50)\n",
    "tfidf_vect.fit(data)\n",
    "train_x =  tfidf_vect.transform(x_train).toarray()\n",
    "test_x =  tfidf_vect.transform(x_test).toarray()\n",
    "sc = MinMaxScaler(feature_range=(-1, 1))\n",
    "train_x = sc.fit_transform(train_x)\n",
    "test_x = sc.transform(test_x)\n",
    "print(train_x.shape, test_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1080,
     "status": "ok",
     "timestamp": 1593526208783,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "_FW4PRXuZmyl"
   },
   "outputs": [],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [200, 250, 300, 350, 400, 450, 500]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt', 'log2']\n",
    "# different criterion\n",
    "criterion = ['gini', 'entropy']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [2, 4, 8, 16, 32]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 6506636,
     "status": "ok",
     "timestamp": 1593532718290,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "Yv2fFag1We_9",
    "outputId": "cf3e6ba4-1dec-4c2f-c565-6930bf5373ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keyword1\n",
      "Fitting 2 folds for each of 100 candidates, totalling 200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.5s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   21.9s\n",
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   29.0s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy:  0.631578947368421\n",
      "test accuracy:  0.8421052631578947\n",
      "keyword2\n",
      "Fitting 2 folds for each of 100 candidates, totalling 200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   17.9s\n",
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   23.3s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy:  0.8421052631578947\n",
      "test accuracy:  0.7368421052631579\n",
      "keyword3\n",
      "Fitting 2 folds for each of 100 candidates, totalling 200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   17.3s\n",
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   22.7s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy:  0.7368421052631579\n",
      "test accuracy:  0.5789473684210527\n",
      "keyword4\n",
      "Fitting 2 folds for each of 100 candidates, totalling 200 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:   17.9s\n",
      "[Parallel(n_jobs=-1)]: Done 200 out of 200 | elapsed:   23.3s finished\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy:  0.5789473684210527\n",
      "test accuracy:  0.5789473684210527\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "accuracy_array_train=np.zeros(4)\n",
    "precision_array_train=np.zeros(4)\n",
    "recall_array_train=np.zeros(4)\n",
    "f1_score_array_train=np.zeros(4)\n",
    "\n",
    "accuracy_array_test=np.zeros(4)\n",
    "precision_array_test=np.zeros(4)\n",
    "recall_array_test=np.zeros(4)\n",
    "f1_score_array_test=np.zeros(4)\n",
    "    \n",
    "    \n",
    "predictet_labels = np.zeros([label.shape[0], 5])\n",
    "predictet_labels[:, 0] = range(1, label.shape[0]+1)\n",
    "acc_train=np.zeros(4)\n",
    "acc_test=np.zeros(4)\n",
    "for i in range(4):\n",
    "  train_y = y_train.iloc[:, i+1].values\n",
    "  test_y = y_test.iloc[:, i+1].values\n",
    "  print('keyword'+str(i+1))\n",
    "  model = RandomForestClassifier()\n",
    "  rf_random = RandomizedSearchCV(estimator=model, param_distributions=random_grid, \n",
    "                                 n_iter=10, cv=2, verbose=2, random_state=42, \n",
    "                                 scoring='accuracy',n_jobs=-1)\n",
    "  rf_random.fit(train_x, train_y)\n",
    "  best_param = rf_random.best_params_\n",
    "\n",
    "  model = RandomForestClassifier(bootstrap=best_param['bootstrap'], \n",
    "                                max_depth=best_param['max_depth'], \n",
    "                                max_features=best_param['max_features'], \n",
    "                                min_samples_leaf=best_param['min_samples_leaf'],\n",
    "                                min_samples_split=best_param['min_samples_split'], \n",
    "                                n_estimators=best_param['n_estimators'],\n",
    "                                random_state = 42)\n",
    "  model.fit(train_x, train_y)\n",
    "  train_pred = model.predict(train_x)\n",
    "    \n",
    "  classfi_report=classification_report(train_y, train_pred,output_dict=True)\n",
    "  accuracy_array_train[i]=np.sum(train_pred==train_y)/train_y.shape[0]\n",
    "  precision_array_train[i]= classfi_report['macro avg']['precision'] \n",
    "  recall_array_train[i]= classfi_report['macro avg']['recall']    \n",
    "  f1_score_array_train[i]= classfi_report['macro avg']['f1-score']\n",
    "\n",
    "  predictet_labels[y_train_id, i+1] = train_pred\n",
    "  print('train accuracy: ', acc)\n",
    "\n",
    " \n",
    "  test_pred = model.predict(test_x)\n",
    "  classfi_report=classification_report(test_y, test_pred,output_dict=True)\n",
    "  accuracy_array_test[i]=np.sum(test_pred==test_y)/test_y.shape[0]\n",
    "  precision_array_test[i]= classfi_report['macro avg']['precision'] \n",
    "  recall_array_test[i]= classfi_report['macro avg']['recall']    \n",
    "  f1_score_array_test[i]= classfi_report['macro avg']['f1-score']  \n",
    "    \n",
    "  acc = np.sum(test_pred==test_y)/test_y.shape[0]\n",
    "  acc_test[i]=acc\n",
    "  predictet_labels[y_test_id, i+1] = test_pred\n",
    "  print('test accuracy: ', acc)\n",
    "  \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show result train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:34: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:51: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:67: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FrG37Wkg9p4Vpvd7WoQxR3g2cCVwt6RfA9bav67J2U3oE3duCcvW/MrCm7YslXUM5y/knnbZswDRXrstRNjid2dw2D3inpDVs3zr00K7aOGhaPacNgFMkvYYSqjOBVwIvBc7tqHl9rwnSNSQ9QNnDMrNZAfgFSb+klJcY6vWLjl6buTLq3t+Az1JC+feS9qBsOX+87fs6bdmAGHYltSWwFvAtSS8YKurVCoFcuY6RSjXcyc2P3wH2Bd5AOXb2Ssok/NCcSz5LRrYh8CFKz3QecH8zrIbtPwOPsH1N83Nnq62yaqhjkqYA2L5b0p6UHYfnAz+1fU6njRsgkh5v+xJJa1KG17akjMXeClxPWaJ3WZdtHDSSdqesarkZWN723yStbPuupuzJ520/rdtW9j+VSsJfBDYC5lMu/n4GvASYZft5Xc+vJAg60FpKtgtlt+u7gY/Y/lzHTRtIzVX/DygT7idRSkjc2OzYfhZlDubzXe3aHFSSNqcMW7yaMmd1JWV1y9m2r5C0ke252TuwaJK2t/1rSf9B2UD2dErIfsv27K5XsSUIOtBa3XIGZUfh/wCn2/6GpHdR1mWnNzBGTa/qiZTJ9WcD04HLgWMpG/RoJo5jnJoNULsAl1LWu28ErALcAnzc9l0dNq+vtS749gbWsv355vYNm0n3lfvl+cu4XgeaEHg0ZWfh3ykfYkc3d+9BKTQXY2T7btvnAUdRVgx9HJhDWelyGrBdd60bTK3VQnsBd9r+FfBt4HBKbaHL++VDrI8N9ZLeARwvaQ1JhwK/kLQ/ZfVQX8iqoe5cRfnw/zRwpu1bm3HX5Wyf323TBlMzuX49cL2kCyjL9WbSrGrpehx2kLSGKaYAKzS33U7ZrX1xs8s4RtFc8K1P2WdhyqTxrZTKAUdSdhf3RdnuBEF3dgLuouwyXEvSvZRu9xGdtmqADa1csf2g7QWUN9m/3mgJgfGR9DhKdcztJC0ALgL+afuG5vmNhZC0AqVW2DWUnupRwN9s797MvazsPjq7IXMEE6g1N/BU4L2292xu34VysMeRwLXZ9bpkmuWky1PKTecFvhha5U+eQFmB9XzKVe1VlBVYZ3bawD4maUXg7ba/3Kxie5Dy3GCoGbwAAB2SSURBVD3QrLj6KuV9/r9dTxIPSY9gYg1tGFkPmNeUoL3b9rHdNmswtT6sVqH0pp5MmXS/mnI1FoupeV7XArYC/mL7h80qrDfR7ODOUNtCrQJc0gwL/R/wY8qBM5c0+zK+BsxtHtsXq63SI+iApF9RwuD7lDN0rwHuyBK88ZE0yfb9kr4FrArcSdnA8w/KLs4fe1iZ5Bhda6XLS4A3A9cBz6EsYHiv7V902sABI2kHyqqrmZTe1DmUwpJXdNqwYdIjmECt9davoqwj3oPS5b6EUrwrJSXGaCgEmh8fTyl3cBuldPezKIepX0cJhBi7oSvDNwDfsX00gKSXAS+T9AfglvQERtesujrI9j7Ar1TOI94VeAVlQ1mCoEZNN/rBZsxwNeBC2yc0uw73pKzRjrF7h6S9+He3+4GmhsuFkv4GHOOc8zxuzWt0MiUQrmnd/lNJ+wGbZn5gTNamLAJ5mu2zXc4j/oGkX9i+sevGDZcgmCDNJPE6wImUN9g8SbcBs4Ejh+qNxJgdTFmHvT2ld7WVpC8Cv242jyUExqk15j+JUlvosyonkp1EKTQ3JSEwOklPA+bbvlzSmcDWwNlNDbFdKKXR9+2yjSPJHMEEkDSlqSW0G7AD8N+UZXmbUCY577O9X5dtHCTtkgaS1qVcvb6B0u1eiVIG4e3OOc+LRdJXgC9RPsT2A9agFE37g+1j+mWlS7+RNJ1SZvoyyn6Lmykf+utSjkw9Hfit7dv6rSxHgmACNKUkrqSsZDnW9jHN7ctRJpEmNTtjYwyaD/8bKTuI17f9+tZ9TwFeYHv/jpo3kFpLm9cDDrH9ktZ9q1MuVvpmJ2y/alZWPZuy2moVyvnOJ1IWhVzYr4sXEgQToHlxPI9Stndn4JuUUr6pJ7QYJO0KvI4yKfxJyvM52fYtkl4EXJJKo+PTWoq7B6XI3FHAz4G7cvW/eCQ9llLe5GmUecEj+nXVVYJggjSbyP5J2Ti2C+WqYUXg27a/0mXbBkmrDPLzgXdRxrPvpRxEczpwHLCT7dkdNnNgSfofyrm6q1POGvgNZZhjXlYKLVyrR7U2pXbYTMrKoHMpBfqeQblAub4f918kCHpM0lTb81VOI3rj0LZylXNKnwPc2xT0ijFoVgqdQul2r2L7PEnbUOYH1gAusP2FLts4aFofYk8HtrL91aYMwssoV7MGXmb7zk4b2sda+y8+CTyKMi+wMmUI83LgZ7b/0GUbR5NVQz3UjGV/SdKOwO08dDne7ZI2Ag7rqn2DpikdcYPteZI+QTkCcBbwe9v7Dn9sv1119bGhHe+PpalI3BQ+PB9A0izbd+Y5XbjW8NmulB7VzyhnOj+GUlfsDOjf12V6BBNA0uHA5pRVQhdTTiu6F3if7W07bNrAkvR4yqqWp1CuvG6mlJ7+bsa0x6/ZAPUHysXKu/tt5+sgkDQT2Af4IGV10BbN7T+hjAbcOtq/71KCoIdaE3Ab2Z7b3PY2Sn3y64D9bf+y00YOkFb3+7OUcr5Dcyv/ATwTuNn2wZ01cEA1q9dEqSO0A7AF8Dvgh8ApCdZFk7SC7fskrQE8QHltngPcA7zZ9jP6tTcACYIJM/RCaf08JcvxFo/KcX9voNS/+YHts5vbV8kQxuJryiDcDTyScnLe3sBPbL+v04b1sWZy+E2Us8bnAZ+2/dtm9dpnKWFwmO2z+nn/RYJgArRfAJImAQ/202aSQdDqDQytGnoiZbJ9OmV99tGL+BUxTGuS+BGUZbjLARvaflHrMavavqPfNkD1i2aV1RMpe1peS9k3sJfLIT4PuwDsVzmqcgK0rwJs35831Pi1nsOTJf2Zstb9ZZSr1qOayeMYn6H3/5solVsvpQxlIOmZknZtauSQ1+xCvQj4ku1LbH+YUlZ6B/jXxUvfhwBk1VBPtOYGplNeKCsDx9u+tOOmLQs+Tvnw/wfwKcpk8UzKklJy5Tp2rXDdllL48MOUUhJQVr8A/DxDbSNrNoytDjxJ0hRKVdHHUPZe0K/DQCPJ0FAPtLrcv6Xs0Bya3Lyq+fnbtm/pso2DpDUstJbtm5rNeR+hLMn9nO0LOm7iQGt2E+8AbGv7Mc1tfwDeYvuChOvImtIbr6BMrq8KTAY2BnanHDh1bYfNG5cEwVLWCoEnAIfafm7zptqRclD9K4BnpHcwfpJ+SCnX/T3KUMbbKQfU72n7d122bdC0eq2Pomx6+jTlSMqbKCuI7rT9ui7bOEiaDXhbUzbg3QPcQVnKfGGnDRujBEGPSHodMA04G/gv2zs34fBm2+/vtnWDSeUs2KdQrsBuoxTxeznwYdtzMoQxfs1Fyq6UMghPp1zZ3gSc1QRFegOjGP78qBxa/wzghcBXbV/ZWePGIUHQQypnEj8a+ATwR8pY7Jm2P91lu6JukmYA7wc2AKbafuaw+7ezfWoHTRsYkp4DXOTmkJlm1zuDeiGSVUM9ZPsu23+lzAusDZwHfKPbVg2moTeapOWbDVBIWm7o9hg7238HDqL0WGdIOk3ShySt2cwXfLzTBva5Zr/Fp23fKOkZzZLwf/VGh16fgyQ9ggkiaXXnoJSlKkNBS0blNK1zKfNXe1NWX/2FUi75uH7eANUlSc8GDqWcMf5C209t3TdUq2mghtMSBD3U1G8ZqGVk/aQ1obkZsBZl6/71wD9sL+i2dcseSasA69j+R9dt6XeStge+SzkS9URKGfSTXY5JHTgJgqWkfXU6tMyxfR+wvO37O2vggGomiM+mTGb+kxIElzffn2b7rg6bt0zIBcv4NZPCB1IO73kSZcXQCpT9Lf/rPjygfjQJgqWkdfW6H7BZ83Ua5SSyP3fauAHUej53Bba3/U5Jm1JWtjyF8qZ7x6B1wbskaUNgG0pBuWuH96oy1DY+7eFeSetQlo7+B/DxQbvoSxAsRc2L4QLKcZQrA3tQVgqtBmznlPYds9Z+jK9QjqF8e/s+4DG2L8+H19hJ2o4yEXw7cCFwKuX1esOgfXB1pXWB8lZge+CpwC+Br9s+d6gWVretHL8EwVLQenE8C3ix7Q+27lsBeJ779KzSftY8dx8EXk8ZDjqDUqrjT122a9BJ2gp4KeXqdQHlef09MNupiLtQrYuTdYHfUi74lqe8Pl8OvM5NJdxBkyBYiiQdQVl5cSBwEnD7oBSd6mdNzabNKV3vpwJ/t/3Wbls1eEbaHNasgHkZZcho10HZANWFVhDsBexs+5Wt+14CvMj2m7pr4eJL0bmloDU88X3KG+pNlCuu0yWdCZyfsezxk/Qk4CWU3sBNwAHA+pSaLikwNw6tek3PAZ7d3HwxcLbt90paKSuxRtcagjwDeK6k59r+TXPbUyhlJR5Sdn5QpEewhEYao26GNJ5LqU++vO09O2ncAGoNs20BfIkSAvdS5lyuAD41aG+yfiLpHOBwSuXW31KqZ55HqYs1t8u2DRJJ+wJvpKwSugZ4HLCv7b8N4gVKgmApUTmgYk1gJeAi4Fe2L5O0ou17um3d4GgFwf7A9ba/0Nz+VMrhKZ9KgbnxaQ1pPBd4u+1XNoGwE6X8ycbAbm4OU4mxafZd7EypefWrQX7+Bm4rdD9plTp4HmVZ48WU8wceB3xf0kHkOV5ctwBPkLS+pEm2z6FcfQ0dCJ7SEmPU6rE+FjimWZJ7ge0bKJVc/2H79jynY9dcsNxp+2jbPx305y89giXQutI6GDgBWIMyofleSn2hc2x/vss2DqpmKe7ngDmUcxymU6pk7mL7uiwbXTzNh9V6wP9RhtrWB75h+7uDOKQxEVrv80cCq9m+bPh9HTZvqcjV6hJoXhwC/k4ZZ92Kstv1QcoVbbbqj4OkYyU9UtLjm6vV/Sjd7s0oezE+kRAYn1avdYqkqZSe6xTK/NUfgGOAH8Dg1ceZQBs1z90BlNdi2/IdtGepS49gMbXGsp9MmRA+X9LzKW+sM4BHAc8axM0lXZH0SNvXSroMWAX4OvA129d13LSB1XqdfoGy9PZySrheCBycD//RNQs/dgSeT9kr8J+UndnX2L5X0quA39m+usNmLrEsH11MrTfQuyj1Rs63/UuVk4qeC1yYEBi75gPrWkmPsr2JpGcA7wb+Kuka4FtDE8cxNpKm2L67qSW0DeWwlHUo5xC8E7gLOCI9rIWzfZ+ksyhzUzdQ9gltBcyVdCvwGWBGdy1cOhIES0DSIyjDa3cO3dYswcsyvPFbEbgb+IKkrzUrg34PIOkVwIbN9xnHHrs3Nr3UOcCfbM8H5lPCFcrQ2xEJgdE1w5SfkPRlymfmDpRg2Ag4sOkZDNzegbYEwZLZBNgTeJWkQ4BDXA79iHGQtAbwGUnPBB5h+9XDHjIf+ClkHHucTqecn/tEYDeVonOHAb+hfIidA4O5AWoitCaJVweeRzmC8lLgONs/GvbwgX5dZo5gMajUIv+r7auan58BvIfS/Tawu+3TumvhYJL0Xf5dpO80yqqha4ATbM/srmWDqxnjXpVy0fI0yofZzpRewktt/z1DQyNrza/8D/ACyqT6TEqw3gD81PZRXbZxaUmPYJwkPRb4LDBP0vmUjSRn8u9hjNcC8zps4sBpfRB9zPYVktaiLMH9KXAtZdI4V67j0BpCW46yf+BuyrnZx1JKoUwd6r0mBEbW6n2uAbzbpbro6pQlt8+n+fxcFoYr0yNYTColfV9GOZTiBsp2/d/Y/kunDRswre73FOAxlDfdpc24LM2yvVuaSbtcuY6TpG8Cm1JqNf0VONf2DyWt4BREXKjW6/KxwNeAn9s+qHX/JODBQQ+AIekRjNNQ+ts+FTi12Wb+YsoSs7dK+pDt47tt5UBZjnIE5X9RrrReAlws6c/Ar4Gzhj6wEgJj0xrSeAGwse2nNRv0dgA+LOkmpyz6qFqvtSmUUt3vaibef0iZI7ijs8b1QHoEi6lZkuf2FYGkDYAbs2x0/CRdYPspko6lHPSxLbAd8HL/u8JjjEFTkuN+SW8Gnm77ja37XkE58S1lvMdB5cjUNwCvpOzHeKXtk7pt1dKTHsFiGhqrbnYWTwLud2q5L5ZmtdAVzTDQ+ra/CnxV0o8pO7ZjHPzv08ZOBJ4v6QOUOawHKKfmHQ+Zc1mYVo/qSZRJ4i0oy2+/BHxN0kaUs7OXmRITCYLF0LwQrrJ9b/MiyFjrYhh6E9k+U9IcyuqWayTtQ9lXINu3Litvtokg6e2UeZav2L5K0gcpmx7fAEyjVMb9DuSw+lEMFY/bD/gzpaLwKgDNcNvFtu+EZWe4MrWGxqgZCkLSnsD7mk0kq0h6tsqh6jFOzWTcqpJOowypzQUOoRyhuC3wjeaheZ2O3VWUUgjXSPolZY7gPcA7bO9i+78SAKNzOcBnMrCV7QOAtYDjmrvfQTktb5mSOYIxanUXf0652jpV0tco2/VnUwqi5Q02Rq3nc3vKOc/vbm5fEZiWYbYlI2lN4O2Uw1PWoFQb/Z7tM5aF5Y69JmlVSl2hu4EdbW/TLAw5E9ja9r2dNnApy5XW2LnZAftIYAVJX6dsdvoQpbbQVl02btC0PoieB7xc0t5NbZx7EgKLp1nSiKQNbd9i+zO2NwGeQ3mvfw2yO3ssmlVBp1LOF7lf0ucoJ+b9caikRKcNXMrSIxgnSS+nLBdd2eWkp7Uom8oSBOPUTLQ/kzIh9wTKWOyFwK+b5bmxGCSdSjlD90jgcNvnD7s/PYIxkrQV5aCpJ1A25J1m+85l7TlMEIyBpL9SljQeaPtySZOHuoaSPgSsbfsDnTZygLQnf5ux2FUoVTEfTSnTsX57yWMsWmuo7THAByhzBU+iLMG9BfgC8J1lbUhjaRpaRSVpN8qQ2nWUoaArgHOBO5bV4d8EwRhIeg7wZkqNlr9TVl0cYfsOSesC99m+qcMmDiRJ76d88K8IvNn2PyWtDKxo++Zl7aqrl1pB8CXgJtv/29y+GmXSfUvgSsoJb3eO8quqJ+n3wIHAypSlo6tSznA4zOXI1GVO5gjGwPZvbb/W9lqU+uMvAa6UdCYwMyEwds1wEJK2pgyxfRB4PKV207qUDTt3QMayx6P1XN0CrCZpctPzuh34C/AWylXti7pq4yBogvNc20fZ/qbtd1JOJhs6MnWZlCAYJ9vH2H4u5Qzdkyn1cWL8tqMUP5tOqdH0IGXH5t6pgbNEDqG8Jg8BdpX0Vsowx+8pyx5zfOoIWpO/jwNWl3SepF0BbP/Z9uc84KeQjSZDQ9GJpm7L1sBuwAds/6pZiXWp7S9m1+v4tQqlPRJ4EyVY/wicTTmi8ljbw8/cjRZJ/6SsDloTeBZl6e1lwH7LchBkZ3F0wuVYz80oBb22bHYTQ1mOCwN+0EcXmhCQ7WuBoTmCoQnQacBrum1hf2oF6EzgVNtfHrqdEqa7U/YTLLPSI4gJ03rDrUypkX8DZfnoo4BLKOc+X5eSEkum2U+gDLGNTet1uS/wKsqQ5UnAtbWsskqPICZM82ZbCfgmZWnjrZTjFI+zfTYsO0W8uiBpqu35/nfRuTyfY9B6fuZThtG2o5zodqGkKyibyO7pqn0TIT2CmBCt5Y2vA15oe3dJ04HXAq+nlPW9oNNGDpjWlew6wIeBFSgF0y4ETrF9aacNHAAjBWWzem0HSr2rScAbl/UVbOkRxEQZerNNo1R0xPY8yqH1KwGvABIE4yPK87o3ZefrQZSNeTOBbSXNsf3/OmzfIBClfMx7gfUoewb+BJxs+/uSHtVcwCzTe1oSBDEhmivXNSlXWRtIuo3yhlsAPJmyVjvlD8ah9TzdR1nV8pdmHfwZlL0Zt0Ge09E0H/LrAe+n9KoeoBzt+clmsvh9Q4/rrpW9l6GhmBCtoaEnUuYHXkgp77sBZWLue8A1tm/rsJkDR9ITgIspJVDeYPua1n2ZHxhFa0XVcykbQw9qFjJMBWYAG9r+XqeNnCAJgpgwzZtse8qhPudKmkEZEtqU0js92vZxC/8N0db6IHsBZZ5lZ+BSSgmUQyiHq+cNvgiS/kSpd/UG22e2bv9XTbFlXYIgeqrVE3gy8HngauAZwGTgI8047FrAjpSt/Zd02NyB1JzhgO17moJp+wMH2D6o25b1r2FLmbegzLO8HLiWEqRft31jl22cSAmC6KnWVeuXgRtaxdC2A/YC/sf2Pztt5IAZFq7vp0wQX0spKvdp2/e1PugyPDSCYUX6zrT9k+b2F1E2NU62vXWnjZxAqTUUPdUqE7EWpZzv0JvwVGB1yuQxkvJaHL+PUVZafYKyN2Ma8LmmtDew7Jypu7QNm/z9G/yrl3C87WcBT2tuW6YOoFmYrBqKifJV4IBm5dAPJT2WsmnnBFj2V2UsTc2V7CTKpObB/vfZGH8Gfgo8yfafumzjIGh6VG8Fni7pA7bPGLpv6PVYS72rXIXFhLD9R+A9lI06f6KURT7U9o3pDSyWB4HfAKdK2l7SSi7HKz6OsqEsRtFc/V9IOSr1t8AXJJ0haX9J63fcvAmXOYLoqebKdXtK4a5bgEMp1RxXbj64YjE1z+07gY0oNZuuB35r+zOp3rpoklYHHkHZO3AL8B+UA6i+bfsXNc2vJAiiJ1qTxK+nVL38OmX/wJ6Ucsj7ddm+QSdpku37Ja1BCYK7KLtk5zTPezUfYuPRel3uSCkw90TKGdkflrSKKz29LV3y6JWhMf9XAF92OdDnY5SyvutKenpnLVsGDBWWs32r7fNtX2r7kqFeQEJgoYZel/+Pcu7AFZSeFMCrJW3TSas6liCInmiWLi5HOR5xRuv2WymH1C8P/z66MsZO0n9KmtJ1OwZRq9TJrc0cwaOB7zZ3v5HmM7G212VWDUXPNKtbvgccI2kP4EfAasCkoRUauXIdm9a+gMcDO9vev1na6Ky4GrfbgBMknQzcafuWpvTJKrZ/A/W9LtMjiJ6R9Gbbl1F2bh5OOfrvesqVVzVrtJey7YDbJa1t+4GhEKjtCnYJPZmybPliYL6kI4EvNl9Vvi4zWRxLVWvH5paUN9YLaqnXMhEk7UXZTbwAOA040fZpXbZpkKgc2fkZ4Ku2/9TscF9AM1dQa+8qQRBLVSsIDgLm2f6cpCm275a0A/BU25/uup2DqrnyX46yI/s5lKvbycArbC/T5+ouDZJWAd5GWSZ6NKUkx4JuW9W9DA3FUtW6ovoLsJ6kVVsfUK+grNdOSYlxGBr2aYrz7QnMsn2K7Y8C+wCfa4I2w0OLYPtO21+kBKmB19U4FDRc3ozRK8cD04H9JO3bnAA1CzgGUlJiPJpJ4imU8hGbAmdJmi/pi8Cqtn839Lgu29mvWkG6rqQ9JL2QslDmHkql1l8MVXCtVYIgesL2VcA7KD2AdYGNgbfanp8r17FrXa3uTjni8whKyO7c3DY7vavRtQLyJZQFC++jLF54EDiTMjdwT83PY+YIoueyy3XJSfomJQS2o3y2/a+kNwG32/5RSkosWlOVdQXbdzY9rBVs3yZpxaEgqLWnmn0E0XMJgSXT9KD+B7gVeArwRElPBd5OKeQH/94xGy2tkhKbUuZX1pN0JXC27ZOgHOjT/G+1z2F6BBF9qrUC62nAPc1yx0nAgZSd2cvbflO3rexvrefw18CvgZubu3YBfmD7h921rn+kRxDRp1pXqFsB+0r6DfBZ4L3ANNtXQobeRtOEwFRgbdufHbpd0hXAWyWd7IqOpFyY9Agi+tDwD3dJa1OGNjYGjrT9h84aN2AkrUOpfnspcJjtuc1+gvNsP77b1vWHBEFEH2qNbb+bUqLjBuAq4AnAy4BP2j6wyzYOEklDB9TfCGxGWTF5ds5uKDI0FNGHWh9MGwFbAr8D1geuA47j3xvzqv8QG0lrbuBRwBNtnyrpC5Qy6BdTal4N9aqqnSQekh5BRJ8ZYVhoFuUIyktsn9tdywZHKwj2o2wa+wfwbeBo23/ttHF9KD2CiP4jwJL+E7iTcsW6PbCVpH9QhoV+3WUD+11rov0JwIspQ0G7A3+W9HvgcNvf66p9/abanXQR/ar1IXYj8ChKTZxTgfOBtYB7IaWnF6ZVUmI7YKbtE2wfZ/vVwIuA24G3NMdVBukRRPQt20dA2RHblPI+WNJU2/Ob+zOuO4LW83INcJmkZ9j+fXPbysAfKcG6H/CLDprYdxIEEX1K0grA/bbvbergeCgEYtFs/1XSH4AvSzof+Cel3tC7KNVHM9/SyGRxRCzTJM2glEAHOJGyn+A4ShHEf3TUrL6SIIjoE62VLpOBZwJr2v6/rts1qBZ2pnPz/D7C9nXdtKz/ZLI4ok+0PrCOBv4DOFLScyQt15RJiHEYOtNZxXKt2+9NCDxUgiCiD7RWumwFTAG+Sjl/4HfAqsDHmxPKYiGGPuwlTZb0XEkvhdIlqLmy6FgkCCL6QGuly9aUHsEOwJ+a2zcFNrd9U1ftGwTpUS2+BEFEf/k+ZUXL4cCPm9veAxwJDzmxLFrSo1oyWT4a0Uds3y7pAMqmsW9Jupyy7v07zf2pKzSC0XpUzaE06VGNIquGIjrWWi20MTCNUmLiMuAOYKrtf3bawAEiaTXgEOCFwO62fyXpKOBM2welSN/I0iOI6FgTAqsDx1ICYB7wAPB34FJJ823f3WETB0Z6VIsnPYKIDkl6pO1rJT0HeKXtfZvziJ8IPBZYxfb7u21lf0uPasmlRxDRrVMk3Umpj38WgO1zgHMkTQMeATmOcjTpUS25rBqK6IikbSjvwfdSPrT+W9Lpkt4iaQ3b19u+BFJgbmEkPbL5dnPgN7ZfQhkGOpcSotslBBYtQ0MRHZF0MHCZ7S83P0+jlEl+EfBk4BTbb+uwiX1P0kWUMxuuB86y/anWfdMopSQuSY9qdAmCiI5ImgccAZwJzLF9Reu+TSnj26dnpcvImh7V14A3AXsAbwBmAz8AfmT71g6bN1ASBBEdkPQy4GPA94ANgbsoZZIvAi5KuelFS49q6UkQRHRA0i+BQ2z/TNLTgc2AxwCTgfsp695/1mUb+116VEtPVg1FTLCmONoJlJr42D4LOEvS2pRAeDblmMqsFlqIpkd1E2WJ6IuBuyS1e1QXDz02IbBo6RFEdGioRs7wD/sEwOjSo1q60iOI6NDCAiAhsHDpUS196RFExMBKj2rpSBBExDIjAbB4EgQREZVLiYmIiMolCCIiKpcgiIioXIIgIqJyCYKIiMolCCIiKvf/Abe0begF9w/jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt    \n",
    "import os\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, accuracy_array_train,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('Accury of all train')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(accuracy_array_train):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' acc_trin_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, precision_array_train,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('precision of all train')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(precision_array_train):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' precision_trin_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, recall_array_train,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('recallof all train')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(recall_array_train):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' recall_trin_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, f1_score_array_train,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('f1_scoreof all train')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(f1_score_array_train):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' f1_score_trin_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show result test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:31: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:48: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:64: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, accuracy_array_test,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('Accury of all test')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(accuracy_array_test):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' acc_test_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, precision_array_test,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('precision of all test')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(precision_array_test):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' precision_test_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, recall_array_test,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('recallof all test')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(recall_array_test):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' recall_test_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n",
    "\n",
    "fig5=plt.figure(figsize=(6, 8)) # \n",
    "key_name=['Is Iran a threat?','cooperation with Iran','The war on global terrorism','The government of Pakistan'];\n",
    "plt.bar(key_name, f1_score_array_test,color = ['black','blue','green','red'])\n",
    "plt.xticks(key_name, rotation=70)\n",
    "plt.ylabel('%')\n",
    "plt.title('f1_scoreof all test')\n",
    "mycolor=['black','blue','green','red']\n",
    "C=0\n",
    "for i, v in enumerate(f1_score_array_test):\n",
    "    v=round(v,2)\n",
    "    plt.text(i-0.2 , v+0.01 , str(v), color='black', fontweight='bold')\n",
    "    C+=1\n",
    "\n",
    "plt.savefig(os.path.join(' f1_score_test_result.png'), dpi=300, format='png', bbox_inches='tight') # use format='svg' or 'pdf' for vectorial pictures\n",
    "fig5.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1193,
     "status": "ok",
     "timestamp": 1593537123952,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "nA-ZLm-NdJnu"
   },
   "outputs": [],
   "source": [
    "cols = ['ID', 'Is Iran a threat?', 'cooperation with Iran', 'The war on global terrorism', 'The government of Pakistan']\n",
    "a = pd.DataFrame(predictet_labels, columns=cols).astype(int)\n",
    "a.to_csv('./drive/My Drive/text mining/pred.csv', index=False)\n",
    "a = pd.DataFrame(y_train_id).astype(int)\n",
    "a.to_csv('./drive/My Drive/text mining/train_id.csv', index=False)\n",
    "a = pd.DataFrame(y_test_id).astype(int)\n",
    "a.to_csv('./drive/My Drive/text mining/test_id.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1119,
     "status": "ok",
     "timestamp": 1593537888526,
     "user": {
      "displayName": "ramin akhtar",
      "photoUrl": "",
      "userId": "09263372638701932058"
     },
     "user_tz": -270
    },
    "id": "WwyGd8RAAuoK",
    "outputId": "8342e72c-dc80-4f7c-8019-04cf09dfbbfb"
   },
   "outputs": [],
   "source": [
    "xl_file = pd.ExcelFile('./drive/My Drive/text mining/label.xlsx')\n",
    "label = xl_file.parse('Sheet1')\n",
    "pred = pd.read_csv('./drive/My Drive/text mining/pred.csv')\n",
    "y_train_id = pd.read_csv('./drive/My Drive/text mining/train_id.csv').values.reshape(-1,)\n",
    "y_test_id = pd.read_csv('./drive/My Drive/text mining/test_id.csv').values.reshape(-1,)\n",
    "# # each keyword accuracy\n",
    "print('each keyword accuracy')\n",
    "columns = pred.columns\n",
    "for i in range(1, 5):\n",
    "  print('--------------------------')\n",
    "  print(columns[i])\n",
    "  acc = np.sum(label.iloc[y_train_id, i]==pred.iloc[y_train_id, i])/label.iloc[y_train_id, 1].shape[0]\n",
    "  print('train acc:', acc)\n",
    "  acc = np.sum(label.iloc[y_test_id, i]==pred.iloc[y_test_id, i])/label.iloc[y_test_id, 1].shape[0]\n",
    "  print('test acc:', acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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